Accurately Determining Crop Yield at a Farm Level

ABSTRACT

Techniques for using a scaling relationship between crop drymass and elevation at a farm level to redistribute crop yield data are provided. In one aspect, a method for analyzing crop yield is provided. The method includes the steps of: obtaining crop yield data for a farm; cleansing the crop yield data using a data filter(s), wherein one or more data points are eliminated from the crop yield data by the data filter; and redistributing a value of the data points eliminated from the crop yield data to data points remaining in the crop yield data to create a crop yield distribution for the farm.

FIELD OF THE INVENTION

The present invention relates to analyzing crop yield for a farm, andmore particularly, to techniques to compensate for missing data based onusing a scaling relationship between crop drymass and elevation at afarm level to estimate accurately crop yield data and thereby produce amore accurate representation of actual crop yield distribution acrossthe farm.

BACKGROUND OF THE INVENTION

Knowing the crop yield for a growing season is important for running andmanaging a farm. To determine crop yield, data collected from the fieldis typically run through an analysis tool which contains filters that‘cleanse’ the data by removing outlying data points and eliminate errorsthat are integrated due to sensor calibration and mechanical defects ofthe machinery. It can happen that only 10% of the yield data may becollected but the yield distribution at the whole farm level may be ofinterest. In such situations interpolation techniques are employed wheredistant data points are used to create intermediary values betweenexisting points. Interpolation techniques may, however, be inaccurate.

Even when the whole data is available for the farm, standard yieldprocessing methods like high-pass and low-pass filters tend to eliminatea large percentage of the data collected. For instance, it is commonpractice to eliminate more than 20%, and in some situations theeliminated data points can be up to 80% of the data points collected.Some of the eliminated data points may be valid but are still eliminateddue to the filter settings, and their order may influence how datapoints are eliminated. There is however a need to create a continuousdistribution of the crop yield data across the farm, as this data maydrive additional prescriptive services such as variable rate seeding,fertilization, management, irrigation, etc.

In order to fill in data points for regions where the data waseliminated by the filters, a simple linear interpolation or Kriging isoften carried out across the remaining data points. However, sinceinterpolation simply takes the distance between existing points andweights them, the resulting yield distribution is oftentimes not a goodrepresentation of the true yield distribution across the farm.

Therefore, there is a need to create yield maps that are as close aspossible to the true yield distribution at the farm level.

SUMMARY OF THE INVENTION

The present invention provides techniques for using a scalingrelationship between crop drymass, and elevation at a farm level toredistribute crop yield data. In one aspect of the invention, a methodfor analyzing crop yield is provided. The method includes the steps of:obtaining crop yield data for a farm (e.g., from a harvesting machine);cleansing the crop yield data using at least one data filter, whereinone or more data points are eliminated from the crop yield data by thedata filter; calculating a total value of the data points that areeliminated (e.g., by adding up the values of the data points that areeliminated), and redistributing the total value of the data points thatare eliminated to data points remaining in the crop yield data to createa crop yield distribution for the farm. The redistribution in locationswhere there is no data available can be based on proximity data sourcesthat are obtained from other sources like elevation. Elevation data maybe obtained for the farm, and a scaling relationship may be determinedbetween crop yield and elevation on the farm using the crop yield dataand the elevation data. The scaling relationship may be used to recreatethe crop yield data for regions of the farm for which the one or moredata points have been eliminated by the data filter. The value of thedata points eliminated from the crop yield data may be redistributed tothe data points remaining in the crop yield data based on the scalingrelationship.

A more complete understanding of the present invention, as well asfurther features and advantages of the present invention, will beobtained by reference to the following detailed description anddrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an exemplary methodology for analyzingcrop yield at a farm level according to an embodiment of the presentinvention;

FIG. 2A is a diagram illustrating crop yield data plotted as a functionof elevation for a first exemplary farm according to an embodiment ofthe present invention;

FIG. 2B is a diagram illustrating crop yield data plotted as a functionof elevation for a second exemplary farm according to an embodiment ofthe present invention;

FIG. 3A is a top-down elevation map of redistributed crop drymass on afarm according to an embodiment of the present invention;

FIG. 3B is a diagram illustrating a linear relationship between theredistributed crop drymass and elevation according to an embodiment ofthe present invention;

FIG. 3C is a longitude elevation map of redistributed crop drymass onthe farm according to an embodiment of the present invention;

FIG. 3D is a latitude elevation map of redistributed crop drymass on thefarm according to an embodiment of the present invention;

FIG. 4A is an elevation map illustrating original drymass distributionon a farm according to an embodiment of the present invention;

FIG. 4B is an elevation map illustrating drymass redistribution on thefarm based on elevation based scaling according to an embodiment of thepresent invention;

FIG. 5 is an elevation map of a farm in which several differentmanagement zones have been identified according to an embodiment of thepresent invention; and

FIG. 6 is a diagram illustrating an exemplary apparatus for performingone or more of the methodologies presented herein according to anembodiment of the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

As provided above, in conventional crop yield analysis linearinterpolation or Kriging is commonly used to supply data for areas wheredata is missing or has been filtered out during the data cleansingprocess. However, the result is not always a good representation of thetrue crop yield.

Advantageously, it has been found that elevation and the weather (e.g.,precipitation) determine more than 40% of the local crop yieldvariation. Elevation products like slope and aspect ratio are alsovariables that affect yield distribution. The elevation slope determinesthe steepness and direction of the terrain change at any point, whilethe aspect ratio describes the direction that the slope faces. Thecalculations of slope and aspect ratio are described, for example, in deSmith et al., “Geospatial Analysis—A Comprehensive Guide to Principles,Techniques and Software Tools, A free web-based GIS resource,” 5^(th)edition, chapter 6.2.1 (2015) (published December 2014), the contents ofwhich are incorporated by reference as if fully set forth herein. Bothslope and aspect ratio are important parameters to determine thedirection in which precipitation or water will flow and the moistureretention properties of the soil. For example, if the slope is downwardand facing South, where exposure to the sun will be maximum, this willmost likely result in lower moisture than a farm area that has zeroslope and faces North and the sun will not reach it to dry out the soil.Slope and aspect ratio can be calculated from elevation data ortopography. Depending on the local characteristics of the slope andaspect ratio, the crop yield will be directly impacted by soil moistureand exposure to sun; adequate soil moisture and exposure to sun willresult in that location having a maximum possible yield. See, forexample, L. M. Thompson, “Climatic Change, Weather Variability, and CornProduction. Agron. J. 78:649-653 (1986), the contents of which areincorporated by reference as if fully set forth herein. Accordingly, amore realistic approach is to leverage the topography at the local scale(i.e., at the farm level), along with weather effects to supplymissing/eliminated data and to create a crop yield distribution thatmore closely approximates the true yield on the farm.

Provided herein are techniques to recreate crop yield data at the farmlevel based on raw crop yield data that is cleansed and then combinedwith topography and weather data. The present techniques operate on thenotion that much of the crop yield data eliminated by the data cleansingprocess is not ‘bad’ data and can in fact be redistributed across alldata points (while conserving the total crop drymass at the farm level).Namely, many standard data cleansing tools used are meant for a broadspectrum of applications and as a result have filters that are setconservatively or without a clear understanding of the impact differentsettings may have on the final outcome. As a result, a large portion ofthe data collected is eliminated—including ‘good’ data that is not dueto any equipment malfunction and/or faulty reading. Further, for anydata points that are in fact ‘bad’ data (i.e., data resulting from amalfunctioning equipment and/or based on a faulty reading) theredistribution of any such ‘bad’ data is diluted over the entire dataset. Crop drymass is the crop one is left with after eliminating theother part of the plants that are not valuable.

Further, the present process preferably is performed in conjunction witha scaling relationship that is established between crop yield andelevation at the farm level. The notion here is that crop yield varieswith topography/elevation. For instance, for a particular growing seasonthe crop yield might decrease with increasing elevation. This might bethe case during a dry season when the precipitation is lower thanaverage for that location and the high elevation points will be drier(being that they are exposed to more sun) while lower lying regions mayhave more moisture retained in the soil. These scaling relationships maychange during a wet season when lower lying areas may have water poolingthat will reduce the crop yield. This scaling relationship between cropyield and elevation may be leveraged during the present redistributionprocess. For instance, in the simplest case, the data points which havebeen eliminated by the data cleansing process (see above) can beredistributed evenly across all of the data points. However, to garner amore accurate representation of the actual crop yield, it is preferableto weigh the data redistribution based on the scaling relationshipestablished between the crop yield and elevation. Also, the cropyield/elevation relationship can be used to populate areas of the farmwhere data points are missing (such as where data has been eliminated bythe cleansing process). Namely, as will be described in detail below,based on an area's elevation, the yield vs. elevation scaling can beused to supply a yield value for the area.

Further, other factors can affect the scaling relationship between cropyield and elevation. For instance, the amount of precipitation during agrowing season can change the scaling relation of yield vs. elevation.To give a simple example, during a growing season with a large amount ofrainfall, water might collect or pool in low lying areas. Standing wateris bad for growing conditions and, in that case, the crop yield mightincrease with an increase in elevation for that growing season. Contrastthat with a growing season with a lesser amount of rainfall. In thatcase, the lower elevations will have more ground water than at higherelevations and, as a result, the crop yield might decrease with anincrease in elevation for that growing season. Thus, conditions such asthe weather (i.e., precipitation) can affect how the crop yield scaleswith the elevation. See below.

As will be described in detail below, some additional features presentedherein include soil data, historical trends in yield estimates, and/orhistorical data from neighboring farms, to verify the yield versuselevation scaling relationship. Other features include extending thepresent process to farms which use variable rate management (i.e., wherethe farm is not being managed uniformly—such as when there isprescriptive seeding, fertilizers are being used, etc.). In that casethe farm can be segmented into management zones where local variationare accounted and used to recreate the yield map. As is known in theart, prescriptive seeding involves (intentionally) varying seeding ratesacross different sections of the farm. Likewise, the application offertilizers to different sections of a farm will vary crop yield. Thus,in those cases it may be preferable to look at smaller segments of thefarm (also referred to herein as “management zones”) to better accountfor local variations.

An overview of the present techniques is now provided by way ofreference to methodology 100 of FIG. 1 for analyzing crop yield at afarm level. In step 102, crop yield data for the farm is obtained. It isassumed that the crop yield data has been collected using a method knownin the art.

A number of different techniques are known in the art for collectingcrop yield data. For instance, a yield monitor is a device commonlyemployed on modern combine harvesters that calculates and records thecrop yield as the harvester moves through the fields and collects thecrop. The yield monitor has sensors configured to measure the mass flowand speed of the crop being harvested in order to calculate the cropyield. Most yield monitors and/or harvesters are equipped with a globalpositioning system in order to geographically locate the yield databeing collected. The data collected and stored by the yield monitors canbe obtained by the present system for analysis in step 102.

The data obtained in step 102 preferably includes the crop yield datafor a given growing season. Typically, crops that are grown over aparticular growing season are harvested at the end of that growingseason. It is at the time of harvesting that the crop yield data can becollected (see above). Thus, users who manage the farm can (by way ofthe present techniques) get an accurate assessment of the crop yieldacross their farm for that growing season. Also, as highlighted above,historical data of crop yield and weather may be used in accordance withthe present techniques to verify the scaling relationship that will bedetermined (for the present growing season) between crop yield andelevation. Furthermore, the moisture distribution at the farm level canbe used to obtain locations that have the right combination of elevationand amount of precipitation to establish similar responses in twodifferent years. In general, it is expected that similarities can existbetween different locations at different times for the same farm. Thus,in step 102 historical crop yield data may also be obtained. By way ofexample only, the historical data obtained in step 102 may be for theprevious two years.

The historical data obtained can be from the same farm, if available.However, if historical crop yield data is not available for the farm inquestion, then historical data can be inferred by analyzing data from aneighboring farm that has historical data available. As will bedescribed in detail below, the vegetative index (VI) can be used tocompare the two farms. While absolute values of the VI may be differentbetween farms due, for example, to different management practices, it isexpected that the relative changes are a good representations of thespatial variability of the yield at the farm level. However, it isdesirable to use a farm that is in close proximity in order to havesimilar soil properties, weather conditions, etc.

In case the yield is not available from farms that are situated in theneighborhood, then the area of comparison can be extended. In suchsituations the patterns of precipitation, average temperature, solarradiation, soil properties, elevation, and farm management practices arecompared across larger geographies. If two farms can be found that sharesimilar soil, weather patterns, sun exposure, elevation and farmingpractices, and one has yield data then it can be assumed that other farmwill have similar yield values in locations that share the samecharacteristics. For the present analysis, the original farm that hasyield data is considered and the distribution of the yield data isconsidered and each yield value will be associated for a certain areawhere the crop was harvested. The second farm that has no data will besegmented into areas similar in value to the farm with yield data, andexact correlation will be established between areas that share the samecharacteristics. If one such area is found, then it is assumed that bothfarms in that location will have the same yield. On the farm having noyield data, once a location has an associated yield value, the yield inall of the other locations can be reconstructed using the elevation andyield scaling relationship derived for the farm that has yield data. Onthe farm having no yield data, the local yield variations for locationsthat do not share the exact same characteristics determined by slope andaspect ratio from topography and soil will determine the variations ofthe yield at the local level. It is notable that if all weather, soil,farming practices, seed properties, and topography data are similarbetween two farms, and if the yield for one data point can be calculatedon the farm without data, then all other data points can bereconstructed.

Further steps can be taken to validate the crop yield data. Forinstance, one can validate the the total mass against an independentmeasure like trade in receipt weight measure (i.e., receipt values fromthe trade in stations where farmers sell their crops can be used). Thisdata is aggregated and is reported to USDA that they publish at the endof the season.

In step 104, the current crop yield data (i.e., the crop yield datacollected in step 102 for the current growing season) is used tocalculate the total crop drymass for the growing season. As will bedescribed in detail below, the present data redistribution process isperformed under the assumption that the total crop drymass is conservedat the farm level. Namely, it is assumed that the total crop drymass isa fixed value, and all redistribution of data points will be carried outwhile maintaining the total crop drymass constant across the farm. Tolook at it another way, the value of the total amount of crop drymassfor the farm is a fixed constant in the present process. Thus, apreliminary assessment of the total crop drymass for the farm is needed.This value is calculated in step 104. The total amount of crop drymasscan be calculated in step 104 as simply being the sum of the all ofcurrent crop yield data points obtained in step 102.

In step 106, the data is cleansed. As provided above, data cleansing istypically performed using a data cleansing tool through which the datais run. The tool includes filters which eliminate outlying data pointsfrom the data set. A variety of data cleansing tools known in the artmay be used in step 106 to cleanse the crop yield data. By way ofexample only, the Yield Editor program, available from the U.S.Department of Agriculture, is a tool that provides a variety ofautomated filters for processing and cleaning yield data. Details ofdata cleansing are provided, for example, in Volkovs et al., “Continuousdata cleansing,” 2014 IEEE 30^(th) International Conference on DataEngineering (ICDE), March/April 2014, pgs. 244-255, the contents ofwhich are incorporated by reference as if fully set forth herein.

As provided above, commercially available data cleansing tools arebroadly applicable to a variety of different settings and, as a result,the constraints set for the filters in these tools are not tailored forany one specific application. Oftentimes the filters are set too high.As a result, a large amount of a data set fed through the cleansingtools is filtered out. A portion of the data eliminated by the filtersis, however, good data. Namely, just because the filters eliminate adata point (as an outlier) it does not mean that the data point is theresult of malfunctioning equipment or a faulty reading. Thus, by way ofthe present techniques, these eliminated data points are redistributedacross all of the data points. The effect of any actual bad data (i.e.,data resulting from malfunctioning equipment and/or a faulty reading)will be minimal since the redistribution will occur over the entire dataset and will preferably be scaled based on the yield versus elevationrelationship.

In accordance with the present techniques it is assumed that in step 106the crop yield data is run through a commercially available yield datacleansing tool, and by way of this data cleansing process multiple datapoints are eliminated from the data set. Examples of suitablecommercially available yield data cleansing tools were provided abovewhich may be used in accordance with the present techniques.

Thus, as a result of the data cleansing process (step 106), datarelating to the crop yield of one or more regions of the farm will bemissing from the data set due to the corresponding data points havingbeen removed by the filters. Namely, crop yield data was obtained forthese regions in step 102, but eliminated (e.g., as being outlying data)from the data set by the filters. In order to improve on conventionalprocesses which apply a simple interpolation process to fill in thesemissing data points, the present techniques leverage a scalingrelationship between crop yield and elevation. This crop yield vs.scaling relationship is also preferably used herein to redistribute theeliminated data points across the entire data set (see below).

Thus elevation data for the farm is needed. This elevation data for thefarm is obtained in step 108. Elevation data can be obtained fromconventional topology mappings of the location. By way of example only,tools provided by U.S. Geological Survey (USGS) mapping service, allowusers to obtain the elevation profile of a location. Thus, in step 108,the topology of the farm can be fed into the present system from opensource data provided by the USGS. Also, in the case where historicaldata is not available and a neighboring farm is needed for historicalcomparison, one might also obtain the topology data for the neighboringfarm in step 108.

Next, in step 110 the crop yield data and the elevation data (obtainedin steps 102 and 108, respectively) are used to establish a scalingrelationship between crop yield and elevation for the farm. As will bedescribed in detail below, this crop yield versus elevation scalingrelationship may be established simply by plotting the crop yield dataas a function of the elevation data for the farm after the outlier datapoints were eliminated. As highlighted above, conditions such as theamount of precipitation during the growing season can affect thisscaling relationship. For instance, as provided above, in drier growingseasons more ground water might be present at lower elevations and, as aresult, the crop yield might decrease with an increase in elevation.However, with increased rainfall the pooling of water in low lying areas(which can negatively impact crop yield) might instead make growthconditions more favorable at higher elevations. Accordingly the cropyield vs. elevation relationship might shift to where crop yieldincreases with an increase in elevation.

As is known in the field of statistics, the correlation between twovariables can be represented by a value known as the correlationcoefficient. The correlation coefficient shows the extent to whichchanges in the value of one variable (in this case crop yield) arecorrelated to changes in the value of the other variable (in this caseelevation). As provided above, factors such as the amount of rainfallduring a growing season can change the correlation coefficient sincethese factors can affect the scaling relationship between crop yield andelevation. This concept is illustrated in FIGS. 2A and 2B.

Specifically, referring briefly to FIGS. 2A and 2B, exemplaryrepresentations of the crop yield and elevation data are shown for twodifferent farms, i.e., a first farm in FIG. 2A and a second farm in FIG.2B, wherein the crop yield data is plotted as a function of elevation.In each of FIG. 2A and FIG. 2B, the crop yield vs. elevation scaling isindicated by a solid line which corresponds to the slope of the cropyield vs. elevation distribution. It is notable, that while in bothcases the crop yield generally decreases with increased elevation, thescaling relationship (as indicated by the slope of the solid line) isdifferent for the two farms. One potential source of the variation maybe the effect of precipitation during the growing season. Specifically,the slope may change when the weather would be drier or there would bemore precipitation.

Referring back now to FIG. 1, as provided above, it is preferable toverify the scaling relationship established in step 110 againsthistorical data for the farm, if available, or from a neighboring farmwith comparable soil properties, precipitation (i.e., the historicaldata obtained in step 102—see above). See step 112. Namely, crop yieldtrends require data collection over multiple growing seasons. Thus,historical data (i.e., data from one or more previous growing seasons)is often available for each farm. Thus, in the most straightforwardcase, the historical data from the farm in question is compared to thedata for the current growing season. However, if historical data is notavailable for the farm in question, then data from a neighboring farmthat has historical data can be leveraged for comparison. To use asclose of a representation as possible, the neighboring farm should havesimilar crop drymass, soil properties, rainfall, etc. To compare drymassbetween two farms, one might use the vegetation index (VI) as acomparison tool. As is known in the art, the VI is a tool to study andidentify vegetation based on a ratio of reflected wavelength bands fromthe Earth's surface that are measured by satellite-based sensors. Thebasic principle behind the VI is that plants (which carry outphotosynthesis) absorb visible light but reflect light in the nearinfrared wavelength band. See, for example, U.S. Patent ApplicationPublication Number 2009/0022359 by Kang et al., entitled “VegetationIndex Image Generation Methods and Systems,” the contents of which areincorporated by reference as if fully set forth herein. Thus, the VIquantifies the wavelengths of light being reflected by plant matter.Vegetation index data can be calculated from images taken, for example,from Landsat Earth satellite imagery tool available from the U.S.Geological Survey or the Moderate Resolution Imaging Spectroradiometer(MODIS) available from the National Aeronautics and Space Administration(NASA). In different stages of the harvest growth, different indicesextracted from the spectral data can be used. For example, early in theseason a normalized difference vegetation index (NDVI) can be used, butin mid-season the NDVI is saturated and plateaus in value. Otherindices, like Green Vegetation index, will perform better in the secondpart of the season. These indices can be calculated from the spectralbands provided by Landsat and MODIS.

To compare soil properties, one may look at soil properties databasessuch as the Soil Survey Geographic (SSURGO) database available from theU.S. Department of Agriculture. These maps allow one to identify thedominant soil conditions in locations like sandy or clay. They alsoprovide information about the water holding capacity and hydraulicconductivity of the soil at the farm level. The SSURGO data is organizedas polygons which represent certain soil properties, and the data maycontain information about the particular soil types found below ground.By way of example only, the farm used for comparison purposes might beselected from within the same polygon in the SSURGO data, so as toensure a close match in soil properties.

A crop yield/elevation scaling relationship established using thehistorical crop yield data (whether it be from the same farm or from aneighboring farm) is then compared with that for the current growthseason (established in step 110), and it is verified that under theweather conditions it follows either a positive or negative slope. Ifthe scaling is correct (i.e., as determined by deviation of the weatherfrom historical trends) then the scaling is used to fill in the missingdata points from the yield map.

In order to create a crop yield distribution across the farm, valueswill be needed for the regions of the farm having missing data from thedata cleansing process. According to an exemplary embodiment, thosemissing values are recreated using the crop yield versus elevationscaling relationship established in step 110. For instance, given theelevation of a region for which crop yield data is missing, one cansimply look to the scaling relationship (see for example the scalingrelationships shown in FIG. 2A and FIG. 2B) and find a correspondingcrop yield value to ascribe to the region. To use a simple example toillustrate this principle, for the scaling relationship shown in FIG.2A—if a crop yield value is missing for a region of that farm having anelevation of 324 meters (m), then a crop yield value of 30 Kilograms(Kg) can be assigned to that region. Similarly, for the scalingrelationship shown in FIG. 2B—if a crop yield value is missing for aregion of that farm having an elevation of 210 m, then a crop yieldvalue of 15 Kg can be assigned to that region.

In step 116, a sum of the value of the crop yield data eliminated by thedata cleansing process (performed in step 106) is determined. To use anexample, if in step 106 the filters eliminated 6 data points having thecrop yield values of 3 Kg, 4 Kg, 60 Kg, 65 Kg, 70 Kg, and 71 Kg, then instep 116 a sum of the eliminated data points would be3+4+60+65+70+71=273 Kg. Of course there would likely be many moreeliminated data points to consider, and this simple example is merelyprovided to illustrate the concept. The present techniques leverage thenotion that much of the data eliminated by the filters is not erroneous,but rather a consequence of an overly conservative filter constraint.Thus, rather than simply disposing of this data, it will beredistributed to the remaining data points in the crop yield data set.

Namely, in step 118 the total value (calculated in step 116) of theeliminated data points is redistributed to all of the data points in thedata set. This can be carried out in a number of different ways. In thesimplest case, an equal amount of this value is distributed among all ofthe data points in the crop yield data set. Again using a simplifiedexample to illustrate this point, if a total of 10 data points werecollected in step 102 from which the above six data points wereeliminated, then the value 273 Kg is distributed equally among the tendata points in the data set, i.e., a value of 27.3 Kg is added to eachdata point. In this manner, the total crop drymass on the farm (see step104) is conserved.

As described in detail above, the crop yield is correlated withelevation. Thus, for a more accurate redistribution of the data, thetotal value of crop harvested from the farm (including the eliminateddata points) can be summed up and, when the redistribution is doneacross the farm, the total weight is maintained constant including themass of the data points that are added to the map. The total weight isconstant across the farm before the cleansing and after the cleansingprocess. Again, the total crop drymass on the farm (see step 104) isconserved.

The present data redistribution process is further illustrated in FIGS.3 and 4. Referring briefly to FIGS. 3A-D, an example is shown of cropyield data redistribution weighted by the scaling relationship betweenthe yield and elevation. In this example, a linear scaling relationshipbetween crop yield and elevation is present wherein the lower elevationdata will get more drymass allocated while the higher elevation datapoints will get less drymass. The addition is determined by the scalingrelation established in FIG. 2. Specifically, FIGS. 3A, 3C, and 3D aretop-down, longitude, and latitude elevation maps, respectively, of theredistributed crop drymass on the farm. FIG. 3B shows the linearrelationship between the redistributed crop drymass and elevation inthis example. FIG. 4A is a yield map where data is filtered out due tovery small crop yield numbers. The two holes are filled up in FIG. 4Bwhere the values of the yield are obtained from the scaling relationshown in FIG. 2.

So far it has been assumed that the farm is being uniformly managed,i.e., that the same growing practices are being implemented uniformlyacross the farm. However, some farms might employ practices such asprescriptive seeding or different fertilizers in different regions ofthe farm which will affect the crop yield in those regions. As providedabove, prescriptive seeding involves (intentionally) varying seedingrates across different sections of the farm. This is done to maximizecrop yield. Likewise, varying the type and/or amount of fertilizers usedin different regions of the farm will affect crop yield. Thus, in thatcase, to look at the data at the farm level might not provide the mostaccurate representation of the crop yield. One might instead look atdifferent regions of the farm separately. These regions, also referredto herein as “management zones,” may be delineated based, for example,on high or low crop drymass areas of the farm. Take for example the caseof fertilizers. Fertilizers may not be applied to the farm uniformly(for example—fertilizer use may be more prevalent in areas where growingconditions, i.e., sunlight, precipitation, etc. are not optimal). Wherethe fertilizers are used, the crop drymass is expected to be relativelyhigh. A metric, such as the vegetative index (VI), may be used to findthese areas of high crop mass on the farm. Higher vegetation indexresults in higher yield value and the vegetation maps can be clusteredusing values of the VI that fall within a certain range. Thesecategories can be grouped in management zones where the nitrogen orother inputs can be adjusted for that zone to bring yield value up tothe maximum value. As a rule of thumb, 1 pound of nitrogen results in a1 bushel increase of corn. To maximize the yield in that area, thedifference between maximum yield and actual yield is translated into anamount of fertilizer needed, and that amount of fertilizer is delivered.For instance, a threshold VI can be set, and any area(s) of the farmhaving a VI above the threshold are considered to be part of a separatemanagement zone, which may or may not (see FIG. 5) be continuous acrossthe farm. The steps of methodology 100 may then be performedindependently for each of the management zones wherein the scalingrelationship is established (for each of the zones) and theredistribution is applied (to each of the zones), etc. FIG. 5 is anexemplary elevation map of a farm in which several different managementzones have been identified. The notion here is to establish commonsegments (management zones) in the farm that fall under the same yieldcategory.

Based on the above-described process, a crop yield distribution for thefarm is produced. Farming practices can then be altered based on thiscrop yield distribution. See step 120. For instance, in regions of thefarm with relatively low crop yield, one might choose to allocate agreater amount of fertilizer to those regions, and/or implementprescriptive seeding practices in order to boost growth in those areas.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Turning now to FIG. 6, a block diagram is shown of an apparatus 600 forimplementing one or more of the methodologies presented herein. By wayof example only, apparatus 600 can be configured to implement one ormore of the steps of methodology 100 of FIG. 1.

Apparatus 600 includes a computer system 610 and removable media 650.Computer system 610 includes a processor device 620, a network interface625, a memory 630, a media interface 635 and an optional display 640.Network interface 625 allows computer system 610 to connect to anetwork, while media interface 635 allows computer system 610 tointeract with media, such as a hard drive or removable media 650.

Processor device 620 can be configured to implement the methods, steps,and functions disclosed herein. The memory 630 could be distributed orlocal and the processor device 620 could be distributed or singular. Thememory 630 could be implemented as an electrical, magnetic or opticalmemory, or any combination of these or other types of storage devices.Moreover, the term “memory” should be construed broadly enough toencompass any information able to be read from, or written to, anaddress in the addressable space accessed by processor device 620. Withthis definition, information on a network, accessible through networkinterface 625, is still within memory 630 because the processor device620 can retrieve the information from the network. It should be notedthat each distributed processor that makes up processor device 620generally contains its own addressable memory space. It should also benoted that some or all of computer system 610 can be incorporated intoan application-specific or general-use integrated circuit.

Optional display 640 is any type of display suitable for interactingwith a human user of apparatus 600. Generally, display 640 is a computermonitor or other similar display.

Although illustrative embodiments of the present invention have beendescribed herein, it is to be understood that the invention is notlimited to those precise embodiments, and that various other changes andmodifications may be made by one skilled in the art without departingfrom the scope of the invention.

What is claimed is:
 1. A method for analyzing crop yield, the methodcomprising the steps of: obtaining crop yield data for a farm; cleansingthe crop yield data using at least one data filter, wherein data pointsare eliminated from the crop yield data by the data filter; calculatinga total value of the data points that are eliminated; and redistributingthe total value of the data points that are eliminated to data pointsremaining in the crop yield data to create a crop yield distribution forthe farm.
 2. The method of claim 1, further comprising the step of;determining a sum of the value of the data points eliminated from thecrop yield data.
 3. The method of claim 2, wherein equal amounts of thesum of the value of the data points eliminated from the crop yield dataare redistributed to the data points remaining in the crop yield data.4. The method of claim 1, further comprising the step of: obtainingelevation products for the farm, wherein the elevation products compriseat least one of slope and aspect ratio.
 5. The method of claim 4,further comprising the step of: determining a scaling relationshipbetween the crop yield data and the elevation products.
 6. The method ofclaim 5, further comprising the step of: obtaining historical crop yielddata from one or more previous growing seasons.
 7. The method of claim6, further comprising the step of: verifying the scaling relationshipusing the historical crop yield data from one or more previous growingseasons.
 8. The method of claim 5, wherein the value of the data pointseliminated from the crop yield data is redistributed to the data pointsremaining in the crop yield data based on the scaling relationship. 9.The method of claim 5, further comprising the step of: using the scalingrelationship to recreate the crop yield data for regions of the farm forwhich the one or more data points have been eliminated by the datafilter.
 10. The method of claim 1, further comprising the step of:determining a total crop drymass for the farm using the crop yield data.11. The method of claim 1, further comprising the step of: alteringfarming practices based on the crop yield distribution.
 12. The methodof claim 1, further comprising the steps of: dividing the farm intomultiple management zones; and performing the steps of method 1independently for each of the management zones.
 13. A computer programproduct for analyzing crop yield, the computer program productcomprising a computer readable storage medium having programinstructions embodied therewith, the program instructions executable bya computer to cause the computer to: obtain crop yield data for a farm;calculate a total value of the data points that are eliminated; cleansethe crop yield data using at least one data filter, wherein one or moredata points are eliminated from the crop yield data by the data filter;and redistribute the total value of the data points that are eliminatedto data points remaining in the crop yield data to create a crop yielddistribution for the farm.
 14. The computer program product of claim 13,wherein the program instructions further cause the computer to:determine a sum of the value of the data points eliminated from the cropyield data.
 15. The computer program product of claim 14, wherein equalamounts of the sum of the value of the data points eliminated from thecrop yield data are redistributed to the data points remaining in thecrop yield data.
 16. The computer program product of claim 13, whereinthe program instructions further cause the computer to: obtain elevationproducts for the farm, wherein the elevation products comprise at leastone of slope and aspect ratio.
 17. The computer program product of claim16, wherein the program instructions further cause the computer to:determine a scaling relationship between the crop yield data and theelevation products.
 18. The computer program product of claim 17,wherein the value of the data points eliminated from the crop yield datais redistributed to the data points remaining in the crop yield databased on the scaling relationship.
 19. The computer program product ofclaim 17, wherein the program instructions further cause the computerto: use the scaling relationship to recreate the crop yield data forregions of the farm for which the one or more data points have beeneliminated by the data filter.
 20. The computer program product of claim17, wherein the program instructions further cause the computer to:verify the scaling relationship using historical crop yield data fromone or more previous growing seasons.